Untitled

Pain dummy

Pain :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0029.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0029.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 15.172777 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5793236  Bit rate: 22.47 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

Pain :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
SPM12: spm_check_registration (v7759)              18:09:07 - 01/06/2022
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels:   1 regions
ans = 1×1 cell array
    {1×1 region}

Pain :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 20.00% Expected 3.00 outside 95% ellipsoid, found 6 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 6 32 Uncorrected: 6 images Cases 6 14 24 31 32 34 Retained 7 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 56.67% Expected 3.00 outside 95% ellipsoid, found 2 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 2 images Cases 4 7 Mahalanobis (cov and corr, q<0.05 corrected): 2 images Outlier_count Percentage _____________ __________ global_mean 3 5 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 3.3333 mahal_cov_uncor 6 10 mahal_cov_corrected 2 3.3333 mahal_corr_uncor 2 3.3333 mahal_corr_corrected 0 0 Overall_uncorrected 8 13.333 Overall_corrected 2 3.3333

SPM12: spm_check_registration (v7759) 18:09:33 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

Pain :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

Pain :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
    %disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [58×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 2 participants, size is now 58

Pain :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Pain :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 18:09:39 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.001553 Image 1 80 contig. clusters, sizes 1 to 957 Positive effect: 14 voxels, min p-value: 0.00027633 Negative effect: 3086 voxels, min p-value: 0.00000024

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 18:09:41 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×80 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 68 voxels displayed, 3032 not displayed on these slices

sagittal montage: 146 voxels displayed, 2954 not displayed on these slices

sagittal montage: 93 voxels displayed, 3007 not displayed on these slices

axial montage: 554 voxels displayed, 2546 not displayed on these slices

axial montage: 659 voxels displayed, 2441 not displayed on these slices

ans =

fmridisplay with properties: overlay: '/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img' SPACE: [1×1 struct] activation_maps: {[1×1 struct]} montage: {[1×1 struct] [1×1 struct] [1×1 struct] [1×1 struct] [1×1 struct]} surface: {} orthviews: {} history: {} history_descrip: [] additional_info: ''

drawnow, snapnow;

Vicarious dummy

clear all

Vicarious :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0030.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0030.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 15.316588 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5787458  Bit rate: 22.46 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

Vicarious :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 18:10:09 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

Vicarious :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 33.33% Expected 3.00 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 13 38 Uncorrected: 5 images Cases 10 13 15 35 38 Retained 7 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 51.67% Expected 3.00 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 2 images Outlier_count Percentage _____________ __________ global_mean 1 1.6667 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.6667 mahal_cov_uncor 5 8.3333 mahal_cov_corrected 2 3.3333 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 8.3333 Overall_corrected 2 3.3333

SPM12: spm_check_registration (v7759) 18:10:41 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

Vicarious :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

Vicarious :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
  %  disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [58×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 2 participants, size is now 58

Vicarious :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Vicarious :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 18:10:48 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.000862 Image 1 45 contig. clusters, sizes 1 to 286 Positive effect: 2 voxels, min p-value: 0.00024366 Negative effect: 1718 voxels, min p-value: 0.00000000

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 18:10:51 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×45 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 28 voxels displayed, 1692 not displayed on these slices

sagittal montage: 5 voxels displayed, 1715 not displayed on these slices

sagittal montage: 10 voxels displayed, 1710 not displayed on these slices

axial montage: 313 voxels displayed, 1407 not displayed on these slices

axial montage: 299 voxels displayed, 1421 not displayed on these slices

ans =

drawnow, snapnow;

Cognitive dummy

Cognitive :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0031.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0031.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 14.833546 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5786750  Bit rate: 22.46 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

Cognitive :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 18:11:24 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

Cognitive :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 38.33% Expected 3.00 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 10 Uncorrected: 5 images Cases 3 9 10 16 48 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 55.00% Expected 3.00 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 1 1.6667 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 3.3333 mahal_cov_uncor 5 8.3333 mahal_cov_corrected 1 1.6667 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 6 10 Overall_corrected 2 3.3333

SPM12: spm_check_registration (v7759) 18:11:53 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

Cognitive :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

Cognitive :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
   % disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [58×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 2 participants, size is now 58

Cognitive:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Cognitive :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 18:12:01 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.000000 Image 1 0 contig. clusters, sizes to Positive effect: 0 voxels, min p-value: 0.00012136 Negative effect: 0 voxels, min p-value: 0.00011325

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 18:12:03 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

ans =

fmridisplay with properties: overlay: '/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img' SPACE: [1×1 struct] activation_maps: {} montage: {[1×1 struct] [1×1 struct] [1×1 struct] [1×1 struct] [1×1 struct]} surface: {} orthviews: {} history: {} history_descrip: [] additional_info: ''

drawnow, snapnow;

General dummy

General :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0032.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0032.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 14.444639 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5794340  Bit rate: 22.47 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

General :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 18:12:35 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

General :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 35.00% Expected 3.00 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 7 images Cases 6 9 14 32 35 38 52 Retained 8 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 56.67% Expected 3.00 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 0 images Outlier_count Percentage _____________ __________ global_mean 2 3.3333 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 0 0 mahal_cov_uncor 7 11.667 mahal_cov_corrected 0 0 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 7 11.667 Overall_corrected 0 0

SPM12: spm_check_registration (v7759) 18:13:03 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

General :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

General :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
   % disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×60 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [60×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×60 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [60×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×60 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [60×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 0 participants, size is now 60

General:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

General :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 18:13:08 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.001665 Image 1 79 contig. clusters, sizes 1 to 921 Positive effect: 17 voxels, min p-value: 0.00005484 Negative effect: 3308 voxels, min p-value: 0.00000000

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 18:13:10 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×79 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 100 voxels displayed, 3225 not displayed on these slices

sagittal montage: 107 voxels displayed, 3218 not displayed on these slices

sagittal montage: 93 voxels displayed, 3232 not displayed on these slices

axial montage: 594 voxels displayed, 2731 not displayed on these slices

axial montage: 640 voxels displayed, 2685 not displayed on these slices

ans =

drawnow, snapnow;